Size Of Battery Energy Storage System Research Articles

Operational cost minimization of a microgrid with optimum battery energy storage system and plug-in-hybrid electric vehicle charging impact using slime mould algorithm

Microgrid (MG) with battery energy storage system (BESS) is the best for distribution system automation and hosting renewable energies. The proliferation of plug-in hybrid electric vehicles (PHEV) in distribution networks without energy management (EM) puts additional pressure on the utility and creates challenges for MG. This research article proposes a stochastic expert method to minimize the total operational cost through proper EM of a grid-connected low-voltage MG by considering the charging impact of PHEVs with the optimal size of BESS. Three strategies are used to control the PHEV charging demand. Economically improved performance of MG is obtained as compared to previous research without considering the daily cost of the BESS (fBESS) and operation and maintenance cost of different distributed generation sources (OMcost). Then, the study is extended by incorporating these two parameters into the objective function of operational cost. Finally, this article analyzes to what extent the fBESS and OMcost factors raise the microgrid's operational cost. Due to non-linear optimization issues, the slime mould algorithm (SMA) is proposed, which performed better EM with a lower operational cost of MG than other methods.

Customized predictions of the installed cost of behind-the-meter battery energy storage systems

Behind-the-meter (BTM) battery energy storage systems (BESS) are undergoing rapid deployment. Simple equations to estimate the installed cost of BTM BESS are often necessary when a rigorous, bottom-up cost estimate is not available or not appropriate, in applications such as energy system modeling, informing a BESS sizing decision, and cost benchmarking. Drawing on project-level data from California, I estimate several predictive regression models of the installed cost of a BTM BESS as a function of energy capacity and power capacity. The models are evaluated for in-sample goodness-of-fit and out-of-sample predictive accuracy. The results of these analyses indicate stronger empirical support for models with natural log transformations of installed cost, energy, and power as compared against widely-used models that posit a linear relationship among the untransformed versions of these variables. Building on these results, I present a logarithmic model that can predict installed cost conditional on energy capacity, power capacity, AC or DC coupling with distributed generation, customer sector, and local wages for electricians. I document how the model can be easily extrapolated to future years, either with forecasts from other sources or by re-estimating the parameters with the latest data.

Optimization of battery energy storage system size and power allocation strategy for fuel cell ship

AbstractThe fuel cell system (FCS) is commonly combined with an energy storage system (ESS) for enhancing the performance of the ship. Consequently, the battery ESS size and power allocation strategy are critical for the hybrid energy system. This paper focuses on designing a method to solve these two problems. First, a battery degradation model is employed to assess the ESS lifetime. Subsequently, the sizing problem and the optimal power allocation are integrated into a cost‐minimization problem, which is solved by a double‐loop optimization approach. The inside loop utilizes the battery degradation model to calculate ESS lifetime. In the outside loop, a power allocation strategy based on the hybrid Particle Swarm Optimization algorithm and Gray Wolf Optimization algorithm is presented. Finally, the power allocation strategy is extended to real‐time implementation by the equivalent consumption minimization strategy (ECMS) and an improved ECMS is proposed to make the FCS operates near the maximal efficiency point. Compared with ECMS, the operating cost reduces by 0.26%. The result indicates that the proposed method can optimize the ESS size efficiently, and the power allocation strategy can assure the stable operation of the fuel cell ship.

Best Practice in Battery Energy Storage for Photovoltaic Systems in Low Voltage Distribution Network: A Case Study of Thailand Provincial Electricity Authority Network

This research investigated the increases of the voltage profile on the Provincial Electricity Authority (PEA)’s low voltage (LV) network due to the solar photovoltaic (PV) penetration. This study proposed the solution to maintain the voltage profile within the PEA’s standard limitation by using battery energy storage system (BESS) application. The algorithm using bisection method to figure out the optimal size and location of BESS was examined and simulated in different scenarios such as summer/winter and weekend/weekday behaviors. Furthermore, the allocation of a battery in various locations was also considered. DIgSILENT power factory with DPL script and Python are the tools used to cover diverse scenario cases. The results showed that the best practice of how to implement BESS to solve the voltage rise problem was the BESS installation at the distribution transformer and the BESS installation separately at the end of each feeder near the loads. However, the optimal size of BESS installation at the distribution transformer was almost double that of installation at the end of each feeder.

Profit-Oriented BESS Siting and Sizing in Deregulated Distribution Systems

Within the deregulation process of distribution systems, the distribution locational marginal price (DLMP) provides effective market signals for future unit investment. In that context, this paper proposes a two-stage stochastic bilevel programming (TS-SBP) model for investors to best allocate battery energy storage systems (BESSs). The first stage obtains the optimal siting and sizing of BESSs on a limited budget. The second stage, a bilevel BESS arbitrage model, maximizes the arbitrage revenue in the upper level and clears the distribution market in the lower level. Karush-Kuhn-Tucker (KKT) optimality conditions, strong duality theory, and the big-M method are utilized to transform the TS-SBP model into a tractable two-stage stochastic mixed-integer linear programming (TS-SMILP) model. A novel statistics-based scenario extraction algorithm is proposed to generate a series of typical operating scenarios. Then, scale reduction strategies for BESS candidate buses and inactive voltage constraints are proposed to reduce the scale of the TS-SMILP model. Finally, case studies on the IEEE 33-bus and 123-bus systems validate the effectiveness of the DLMP in incentivizing BESS planning and the efficiency of the two proposed scale reduction strategies.

An intelligent energy management system for optimum design and real-time operation

<span lang="EN-US">Planning and management of distribution networks has become a very difficult task, especially with the strong expansion of renewable energy sources (RES) which are intermittent in nature. Maintaining fluidity and reliability of real-time decisions while taking into consideration uncertainties related to production and increasing the profit of distribution network operators is the objective of the system proposed in this work. It is an intelligent energy management system dedicated to the management of grid-integrated RES and battery energy storage systems (BESS), composed of: i) a real-time control and data acquisition model, ii) a model for forecasting the intermittent parameters of RES based on neural networks, iii) a long-term planning model based on the optimal placement and size of RES and BESS, and iv) an hourly planning model for scheduling the energy distribution between energy sources. The non-dominated sorting genetic algorithm and the entropy-TOPSIS method (technique for order of preference by similarity to ideal solution) form the basic block of this model. To evaluate it, a modified IEEE 33 bus network was used for testing and the results, for short-term scheduling, proved that the system succeeds in maximizing profits and significantly minimizing CO<sub>2</sub> emissions, in addition to power losses and voltage drops.</span>

Utility-scale energy storage system for load management under high penetration of electric vehicles: A marginal capacity value-based sizing approach

To determine a trade-off between the battery energy storage system (BESS) size and corresponding benefits in managing the load of distribution systems under high penetration of electric vehicles (EVs), a marginal capacity value-based sizing approach is proposed in this study. First, the yearly load profiles of EVs are estimated considering different factors, such as EV parameters, driver behavior, day types, and customers at each secondary distribution transformer. Then, a utilization index is proposed to quantify the benefits of increasing the BESS size. To this end, an iterative method is proposed to determine the BESS size with a given utilization target. To expedite the convergence speed, a binary and interpolation search-inspired algorithm is proposed, which can determine the BESS size within a limited number of iterations. The performance of the proposed BESS sizing approach is compared with three other approaches, i.e., optimization-based sizing approach, metaheuristic sizing approach, and enumeration-based sizing approach. The obtained results are tested and validated using the data of a real distribution system. Finally, a sensitivity analysis of different user defined factors such as granularity level in BESS size and utilization target levels is carried out. Simulation results have shown that the proposed method can significantly reduce the BESS size while having minute impact on the peak shaving.

A review of battery energy storage systems for ancillary services in distribution grids: Current status, challenges and future directions

Battery Energy Storage Systems (BESS) are essential for increasing distribution network performance. Appropriate location, size, and operation of BESS can improve overall network performance. The appropriately scaled and installed BESS helps meet peak energy demand, improve the advantages of integrating renewable and distributed energy sources, improve power quality control, and lower the cost of expanding or re-configuring the distribution networks. This paper investigates the feasibility of BESS for providing short-term and long-term ancillary services in power distribution grids by reviewing the developments and limitations in the last decade (2010–2022). The short-term ancillary services are reviewed for voltage support, frequency regulation, and black start. The long-term ancillary services are reviewed for peak shaving, congestion relief, and power smoothing. Reviewing short-term ancillary services provides renewable energy operators and researchers with a vast range of recent BESS-based methodologies for fast response services to distribution grids. Long-term ancillary services will provide the distributed network system operators and researchers with current BESS-based bulk-energy methods to improve network reliability and power quality and maximize revenue from renewable energy generation. The review presents a list of energy storage policies and BESS projects worldwide with a cost-benefit analysis. The challenges for deploying BESS in distribution grids recommended solutions for the implementation challenges, and future research directions are also presented.

Data-Driven Optimal Battery Storage Sizing for Grid-Connected Hybrid Distributed Generations Considering Solar and Wind Uncertainty

A large-scale renewable-based sustainable power system requires multifaced techno-economic optimization and energy penetration. Due to the volatile and non-periodic nature of renewable energy, the uncertainty of renewables combined with load uncertainties significantly impacts the operational efficiency of renewable integration. The complexities in balancing demand, generation, and maintaining system reliability have introduced new challenges in the current distribution system. Most of the associated challenges can be effectively reduced by using a battery energy storage system (BESS) and the right techniques for handling uncertainties. In this paper, a distributionally robust optimization (DRO) technique with a linear decision rule is formulated for the unit commitment (UC) framework for optimal scheduling of a distribution network that consists of a wind farm, solar PV, a distributed generator (DG), and BESS. To cut the energy cost per unit, BESS plays an important role by storing energy at an off-peak time for on-peak-time use with relatively lower prices. For the all-time minimum overall systems cost, the distribution system requires an optimal size of the BESS to be connected to provide optimal scheduling of DGs. Three case studies are formulated using an IEEE 14 bus system (converted from MW to kW to match the BESS size available in the market) and solved with the proposed distributionally robust optimization technique to achieve the maximum operating point with an optimal capacity of BESS, i.e., wind, solar and hybrid. Each case study has its own optimal 30-min interval schedule for DGs along with the optimal capacity of BESS. The cost comparison with and without BESS and its impact on the start-up and shut down of DGs is reported with all the dynamic economic dispatch results, including the battery’s state-of-charge profile. The proposed technique can handle the uncertainties in renewables and allows economical energy dispatch and optimal BESS sizing with comparatively lower computational processing and complexities.

Multi-objective optimal siting and sizing of BESS considering transient frequency deviation and post-disturbance line overload

With the development of renewable energy sources, more frequent and severe active power disturbances emerge in power systems, and jeopardize the frequency stability and line loading security. The battery energy storage system (BESS) is able to adjust output power flexibly, and an attractive solution to improve frequency dynamics and power flow distribution. This paper proposes a multi-objective optimal siting and sizing scheme for the BESS to arrest frequency excursion and mitigate line overload under major disturbances. First, an extended average system frequency model is developed to estimate the required transient frequency regulation capability (TFRC) for frequency stability, and the post-disturbance power flows. Then, taking the required TFRC and line capacity as constraints, a multi-objective optimization model is established to minimize the life cycle cost of the BESS and generation cost. Furthermore, based on the linear weighted method, big-M method, and multi-cut generalized Benders decomposition, the intractable optimization model is transformed into a master problem for investment decision making, and a set of subproblems accounting for normal operation, frequency stability and line loading security. Additionally, the master problem and subproblems are solved iteratively to determine the location and capacity of the BESS. Case studies are conducted to validate the proposed scheme, showing superior performance in improving frequency nadir and alleviating post-disturbance line overload.

Optimization of PV and Battery Energy Storage Size in Grid-Connected Microgrid

This paper proposes a new method to determine the optimal size of a photovoltaic (PV) and battery energy storage system (BESS) in a grid-connected microgrid (MG). Energy cost minimization is selected as an objective function. Optimum BESS and PV size are determined via a novel energy management method and particle swarm optimization (PSO) algorithm to obtain minimum total cost. The MG was designed to use its own energy as much as possible, which is produced from renewable energy resources. Since it is a grid-connected system, it can demand energy from the grid within the determined limit with penalty. It differs from the studies in the literature in terms of optimizing both parameters such as PV and BESS size, being a grid-connected self-contained MG structure and controlling the grid energy by an energy management algorithm and optimizing the parameter via PSO with an energy management system (EMS). Results are compared for different PV and BESS. Moreover, effectiveness of the novel energy management method with PSO is compared with the genetic algorithm, which is the one of the well-known optimization algorithms. The results show that the proposed algorithm can achieve optimum PV and BESS size with minimum cost by using the new energy management method with the PSO algorithm.

Wind-Plus-Battery system optimisation for frequency response service: The UK perspective

Battery energy storage systems (BESSs) co-located with wind farms (WFs) can provide frequency response (FR) services to AC grids exploiting the existing connection points. The UK FR market reforms introduce the Dynamic Low High (DLH) product that is procured by 4-hourly blocks in weekly auctions. This paper develops a modelling framework to optimise the BESS size along with bidding and operating strategies of a co-location system for the DLH provision based on the UK perspective. Power flows across the system are dispatched by the designed strategies, and employed to estimate the system's net present value which is then maximised to suggest the best BESS size, state of charge levels for the DLH delivery and WF-BESS interaction, as well as the DLH capacity and 4-hourly blocks to be bid for in weekly auctions. The optimisation results are compared between different strategies and discussed around the feasibility of co-location projects under new circumstances.

Event-Triggered Hybrid Voltage Regulation With Required BESS Sizing in High-PV-Penetration Networks

In distribution networks with a high penetration of photovoltaic (PV), a coordination between reactive power compensation (RPC) of PV inverter and active power compensation (APC) of battery energy storage system (BESS) is always used in voltage regulation (VR). Since using a periodical VR inevitably increases communication utilization, how to design a new event-triggered one to reduce communication burden while achieving comprehensive voltage governance with the required minimum BESS sizing? Therefore, an event-triggered hybrid VR method is proposed in this paper. Firstly, an event-triggered communication is designed based on the changes in bus voltage and sensitivity, thereby defining all operating instants into transmission and ordinary sampling types. For the voltage violation issue at the transmission instants, a network-wide coordinated proportional RPC-APC is then designed, which keeps constant during one transmission interval. For the additional voltage fluctuation at the ordinary sampling instants in the interval, a local RPC-APC is additionally designed to yield a fast suppression without using any communication. In addition, the required minimum BESS sizing is obtained by calculating the total APC for VR requirement, which gives a reference in configuring BESS. Finally, the case study is conducted to demonstrate the effectiveness of this method.

Optimal operation of static energy storage in fast-charging stations considering the trade-off between resilience and peak shaving

Enhanced penetration of electric vehicles (EVs) poses several challenges to the power network, such as uncertain peak loads and resilience issues during outages. Both resilience and peak shaving functions can be achieved by using a local static battery energy storage system (BESS) in the charging stations. However, resilience and peak-shaving are contradictory, i.e. increasing one will deteriorate the other. Therefore, in this study, a resilience and peak shaving trade-off scheme is proposed to optimally utilize the static BESS. Firstly, a resilience window is formulated to determine the amount of energy to be stored in the BESS for the resilience of EVs in the case of any contingency. During peak hours of the day, more importance is given to peak-shaving, whereas in the off-peak hours, resilience is prioritized. Then, an optimization algorithm is developed to minimize the cost of the system while maintaining resilience and maximizing peak shaving. Using the proposed window method, an optimal window size has been determined via a normalization approach. Simulations have been carried out to show the effectiveness of the proposed scheme by considering the conflicting nature of resilience and peak shaving. With the help of the proposed strategy additional 3.9 % of peak shaving and 3.41 % reduction operational costs is achieved. Moreover, sensitivity analysis has been carried out by considering different factors (market price, size of EV fleet, and BESS size) that can affect the optimal size of the resilience window.

Battery Energy Storage System Sizing, Lifetime and Techno-Economic Evaluation for Primary Frequency Control: A Data-driven Case Study for Turkey

The share of renewable energy sources (RES) in power systems has been increasing in recent years. Future power systems will have lower inertia and difficult controllability, especially due to intermittent and variable renewable energy that is not dispatchable easily due to its fluctuating nature. Thus, it is necessary to increase the grid’s flexibility to ensure system stability. For this need, new technologies such as battery energy storage systems (BESS) are widely discussed. It is thought to be very useful to create a fast and accurate response in frequency control services with BESSs, especially in low inertia grid conditions. The sizing, charge-discharge control, and lifetime of a BESS providing frequency control service depend heavily on the changes that may occur in the power systems. So, it is a very complex issue to decide on during the investment phase. In this study, the optimum sizing, lifetime, and techno-economic evaluations of BESS providing primary frequency control (PFC) service have been made by grid's frequency data-driven. For this purpose, firstly; the BESS design providing PFC is created for Turkey’s electricity system. Secondly, with the developed algorithm, the number of charge-discharge cycles of the BESS is calculated and the lifetime and capacity fading of the BESS are determined according to the frequency deviation. Finally, economic evaluations have been made for BESS considering the investment- operating costs and PFC market prices.

Optimal Location and Sizing of BESS for Performance Improvement of Distribution Systems with High DG Penetration

This work proposes an optimal location and sizing of battery energy storage system (BESS) installation for performance improvement of distribution systems with high distributed generation (DG) penetration level where the DGs comprise photovoltaics (PV) and wind turbines (WT). The installation of the BESS can reduce costs incurred in the systems, alleviate reverse power flow when the systems are in the high DG penetration level, and also achieve peak shaving during high demand. To find the optimal location and sizing of the BESS, three optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), and salp swarm algorithm (SSA), are applied, and their performances are compared. The considered objective function is the system costs consisting of transmission loss cost, peak power cost, and voltage deviation cost. The system performance improvement is compared in terms of transmission loss, peak demand, and voltage regulation reductions. IEEE 33- and 69-bus distribution systems with high DG penetration are tested to investigate the performance improvement of the BESS installation. The results found that the installation of the BESS could successfully decrease system cost, improve voltage profile, reduce power losses, alleviate reverse power flow, and achieve peak shaving where PSO and SSA are found to be the best competitive algorithms. So, the proposed method can be further applied to find the optimal location and sizing of the BESS to improve the performance of practical systems in the future.

The effect of multi -uncertainties on battery energy storage system sizing in smart homes

Energy storage can provide many services and solve a multitude of problems in today's power grid. Recent studies on battery energy storage system (BESS) evaluation, sizing and scheduling using energy management system (EMS), have focused on advanced modeling and optimization methods to maximize value streams accumulated across multiple services. In this paper, an algorithm is proposed for EMS of smart home community including BESS, photovoltaic system (PV) and electrical vehicle (EV) with multiple uncertainties based on real behavior of consumers. The suggested algorithm reduces consumers cost by selecting optimal BESS sizes regarding the uncertainty of the market price, power generation of the PV, the amount of charge of the EV and the electricity of the grid. It is so crucial that all uncertainties and consumers' life style are considered to achieve accurate results by taken in to account as double apply both in BESS optimal sizing and EMS of smart home community. Genetic algorithm is used as an optimization method for solving of compatibility -based EMS problem in the community of smart homes considering controllable and uncontrollable loads, and it has been developed for three different cases: on grid, off grid, and off grid in addition to EV connected. The introduced algorithm, by calculating the optimal size of the BESS with state of charge (SOC), not only reduces the consumer cost but also increases their profit by selling excess energy to the grid.

Power system frequency stability using optimal sizing and placement of Battery Energy Storage System under uncertainty

In the literature, a variety of system uncertainty is not included in the frequency stability research. This research demonstrated that employing the probabilistic frequency stability analysis approach which accounts for the uncertainty of system parameters such as loads and generation uncertainty, resulted in a more accurate frequency stability assessment when compared to the deterministic technique. Additionally, this study proposes a framework for determining the optimal size and placement of Battery Energy Storage System (BESS) in power systems while taking into account the uncertainty associated with system generation and demand, as well as intermittent generation sources and contingency locations which have been rarely considered in the literature. The novelty of these methods is that they determine the optimal BESS size and placement based on the amount and location of the critical system loads. The methodologies have been validated using IEEE 39-bus and simplified SE-Australian power systems where the simulations were implemented using DIgSILENT/PowerFactory software with Python interface. The simulation results revealed that the optimal BESS size should not be equal to more than 4.5% from the system loads amount while the optimal BESS placement was acquired when the BESS are placed closer to around 30% of the system loads. Furthermore, by investigating the impact of the contingency and wind farm locations on the optimal BESS size, the contingency location had the greatest impact on the BESS properties, while the wind farm locations provided a negligible impact.

A secure system integrated with DC-side energy storage for renewable generation applications

Massive energy storage capability is tending to be included into bulk power systems especially in renewable generation applications, in order to balance active power and maintain system security. This paper proposes a secure system configuration integrated with the battery energy storage system (BESS) in the dc side to minimize output power fluctuation, gain high operation efficiency, and facilitate fault ride through, which is suitable for unidirectional renewable power generation systems (power transfer from renewable sources to the grid). The system utilizes robust diode units (DUs) to protect receiving-end devices against dc faults. Also, the BESS and half-bridge modular multilevel converter (MMC) at the receiving end can operate safely and flexibly to achieve stable and high-quality power transfer, in both source power intermittency and dc-link fault cases. Depending on BESS sizing, the source system power fluctuation can be reduced (absorbed by the receiving-end BESS) when a receiving-end grid fault occurs. Topological configuration and control design of the proposed system are presented. Simulation results show the effectiveness of the proposed system in both dc and ac fault cases, with power fluctuation elimination functionality highlighted. The receiving-end operation losses are investigated, showing a high-efficiency system. In addition, key system implementation considerations regarding the proposed system are elaborated.

A simple and effective methodology for sizing electrical energy storage (EES) systems based on energy balance

The use of the electrical energy storage (EES) plays an important role in the transition of energy generation towards renewable energy sources (RESs). An effective sizing of EES systems is very important in order to cope with the volatility of RESs and to ensure a reliable energy supply. The present paper provides a methodology which helps to determine the minimum required EES size for conceiving a fully standalone system. Its approach is based on the evaluation of the energy balance for a given design period, and it can also be applied for sizing the EES system in grid-connected applications. The methodology was validated using measurement data obtained from two different systems corresponding to: a) a near-zero energy building with local generation sources, and b) a large-scale battery energy storage system (BESS) installed in a factory and used for peak-shaving. The obtained results confirmed the effectiveness of the proposed methodology by estimating the required size of the EES system. A good correlation was found between the estimated and the installed BESS size in the considered systems. The deviation between real and estimated BESS capacities was found to be less than 5%.